Literature DB >> 21315326

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

Stanislaw Osowski1, Krzysztof Siwek, Robert Siroic.   

Abstract

This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21315326     DOI: 10.1016/j.compbiomed.2011.01.008

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  2 in total

1.  Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers.

Authors:  Huifang Huang; Jie Liu; Qiang Zhu; Ruiping Wang; Guangshu Hu
Journal:  Biomed Eng Online       Date:  2014-06-05       Impact factor: 2.819

2.  A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals.

Authors:  Huifang Huang; Jie Liu; Qiang Zhu; Ruiping Wang; Guangshu Hu
Journal:  Biomed Eng Online       Date:  2014-06-30       Impact factor: 2.819

  2 in total

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